The aim of the Condimon project is to develop an integrated sensor system for in-line oil condition monitoring, covering the most important oil condition parameters including corrosion (acidity) for industrial power generator engines or turbines.

including a novel oil corrosion sensor for bio-gas operated power generator engines

Biomass coming from biological material such as manure, organic waste, trees and plants is one of the largest and most important renewable energy options at present. Biogas production has significantly risen across the EU in the past years. According to the statistics of the European Biogas Association today there are more than 13 800 biogas plants in Europe with an installed gross capacity of 7400 Megawatt. By 2020 methane production will expectedly double compared to a decade ago and reach 24.2 billion cubic meter.

However process optimisation through innovative technologies still has much potential to increase the productivity and profitability of existing and newly established biogas plants. For example biogas operations often suffer from high costs associated with quality degradation of oil that can attack essential engine parts. As oil analysis can give a reliable overview of the current condition of the engine it can be considered as an essential aspect of optimally operating industrial engines.

The main objective of the CONDIMON project is to create a validated prototype of a multisensory system suitable for the online monitoring of the lubricant oil of biogas generators running at the facilities of project partners. The prototype will incorporate a novel sensor taht can directly measure the corrosion (acidification) of the lubricant, a critical parameter in combustion engines especially in case of biofuel based operations. Our final expectation is to create an efficient condition monitoring system that returns the investment within two years of for existing installations and can be seamlessly integrated into new biogas based electric generators.

The research leading to these results has received funding from the European Union's Seventh Framework Programme managed by REA-Research Executive Agency (FP7/2007-2013) under grant agreement no. FP7‐SME‐2013‐2‐606080.